Gastos Casa
10 March, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 29/12/2024 | Electricidad | 27417 | Andrés | NA |
| 29/12/2024 | Comida | 83284 | Tami | Supermercado |
| 30/12/2024 | Comida | 30000 | Andrés | nueces almendras mix etc |
| 30/12/2024 | Otros | 47484 | Andrés | viaje a brasil (duplicar para q cargue sobre tami) |
| 4/1/2025 | Diosi | 53999 | Andrés | n y d pumpkin 7.5 |
| 4/1/2025 | Comida | 15260 | Andrés | NA |
| 6/1/2025 | Comida | 40988 | Tami | Supermercado |
| 8/1/2025 | Pago cámaras MB | 20000 | Tami | NA |
| 13/1/2025 | Comida | 67387 | Tami | Supermercado |
| 20/1/2025 | Comida | 21692 | Andrés | gnoccis |
| 20/1/2025 | Comida | 86884 | Tami | Supermercado |
| 21/1/2025 | Comida | 21525 | Andrés | piwen |
| 23/1/2025 | VTR | 21990 | Andrés | NA |
| 25/1/2025 | Diosi | 20000 | Andrés | arena diosi |
| 27/1/2025 | Comida | 71516 | Tami | Supermercado |
| 30/1/2025 | Electricidad | 55000 | Andrés | NA |
| 6/2/2025 | Comida | 52730 | Andrés | supermercado (no cobre el otro de 25k pq muchas son cosas mías) |
| 9/2/2025 | Comida | 12500 | Andrés | NA |
| 17/2/2025 | Comida | 7940 | Andrés | NA |
| 18/2/2025 | Electricidad | 64888 | Andrés | la puse por adelantado para que no se me olvide |
| 18/2/2025 | Comida | 17820 | Tami | Supermercado |
| 23/2/2025 | Comida | 86908 | Tami | Supermercado |
| 27/2/2025 | Comida | 10000 | Andrés | NA |
| 26/2/2025 | Comida | 4620 | Andrés | NA |
| 1/3/2025 | Comida | 2300 | Tami | Supermercado |
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.7803e+08 2 4.9374 0.0074 **
## lag_depvar 2.6254e+11 1 2650.7024 <2e-16 ***
## Residuals 8.0919e+10 817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1913.827 16371.50 0.1521906
## 2-0 31385.708 23152.875 39618.54 0.0000000
## 2-1 24156.870 19385.032 28928.71 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 652 98812.71 2 104812.71
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## 679 40886.14 2 38298.57
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## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
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## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
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## 708 98395.14 2 95424.29
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## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
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## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
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## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
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## 734 45503.86 2 45189.57
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## 736 39131.29 2 54640.14
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## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
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## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 665 53619.97 22361.170
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2022.855870 4042.073016 -539.725460 2436.638053 -2972.901633
## 7 8 9 10 11
## 517.557533 -5657.595813 -1185.724606 -3963.834238 -413.495180
## 12 13 14 15 16
## -4935.868933 -1602.880278 -893.248374 383.466135 -3238.230683
## 17 18 19 20 21
## -371.865118 -2124.882259 6609.885998 -1529.382962 -1207.754816
## 22 23 24 25 26
## 1476.564895 -1187.216885 234.581934 1694.415584 -7104.119658
## 27 28 29 30 31
## 950.567055 8194.116693 413.659477 -18.491193 -2404.738768
## 32 33 34 35 36
## 1574.042583 4569.256991 1120.598928 2384.910401 -1875.621930
## 37 38 39 40 41
## 4602.387642 4300.490037 -2281.332447 -2984.721398 -1110.889734
## 42 43 44 45 46
## -10741.322340 7297.118733 2558.775192 1366.311443 8103.695693
## 47 48 49 50 51
## 679.058485 6522.063894 6703.873678 -5896.326231 -4803.449276
## 52 53 54 55 56
## -5063.292095 -7928.057942 6135.927955 -4075.688785 -4890.629260
## 57 58 59 60 61
## 3862.507580 890.873096 -30.142360 143.929946 -4995.085044
## 62 63 64 65 66
## 18131.506028 3631.767324 -3656.483170 5918.948312 7334.252068
## 67 68 69 70 71
## 14625.166652 1670.865099 -13233.310397 -1314.469634 4637.287642
## 72 73 74 75 76
## -4908.984023 -4408.492401 -10497.586090 2474.647097 -5394.553588
## 77 78 79 80 81
## 1072.474710 -6859.301480 558.246134 -2344.957095 -2683.581409
## 82 83 84 85 86
## -3920.748358 -524.718589 2326.310486 3771.199418 481.480178
## 87 88 89 90 91
## -480.969358 200.042720 4304.734038 -1163.858966 1150.950094
## 92 93 94 95 96
## -2065.280567 -1043.467258 179.074075 275.930805 -7483.265292
## 97 98 99 100 101
## 2398.180167 -8598.646459 -2930.447843 -4028.108546 -1723.413855
## 102 103 104 105 106
## -1248.090220 3193.737592 -2333.240790 2603.601400 -1152.003022
## 107 108 109 110 111
## 976.984623 2592.063615 -3152.044212 -4718.540680 -842.601098
## 112 113 114 115 116
## 1910.942903 11698.598531 -1247.233024 2665.446000 4258.120937
## 117 118 119 120 121
## 3495.321933 -1108.999389 -4723.314472 -3726.463627 2320.676553
## 122 123 124 125 126
## -1733.565492 1341.048543 8857.834627 839.976710 123.642970
## 127 128 129 130 131
## -2527.239754 2651.867395 7047.747238 1002.895179 -8508.464968
## 132 133 134 135 136
## 1747.884366 4133.050018 -3169.263486 -1421.893359 -854.727386
## 137 138 139 140 141
## -3879.955900 1186.145782 -493.634012 -2911.566926 1722.241964
## 142 143 144 145 146
## -1878.814059 -7825.836730 2048.584973 -3473.305592 2110.517668
## 147 148 149 150 151
## -251.898746 1028.059002 -355.742781 1355.541760 1188.467266
## 152 153 154 155 156
## 3357.221739 -4863.864470 -1172.520891 -3233.198999 5961.510764
## 157 158 159 160 161
## 9746.184542 -3627.787657 -4972.524283 3412.432667 -1.209283
## 162 163 164 165 166
## 2498.883085 -6111.249560 -6943.943749 3964.713815 17194.785696
## 167 168 169 170 171
## 3406.085573 -624.022476 -2670.004233 -1324.468954 3371.845674
## 172 173 174 175 176
## -450.989318 -8297.390160 2651.522600 4109.049700 403.539449
## 177 178 179 180 181
## 8527.878392 -9481.457448 -3692.089412 -10960.858165 -11444.074007
## 182 183 184 185 186
## 1042.331607 9095.743653 -1642.081237 5716.816226 6333.625530
## 187 188 189 190 191
## 12925.602138 8177.623944 -4329.176654 2204.825575 10104.740612
## 192 193 194 195 196
## -1922.998552 -2719.608716 -10550.132508 -6615.444900 991.719664
## 197 198 199 200 201
## -5477.073217 -10031.276509 5163.505155 -3297.778563 -1937.564953
## 202 203 204 205 206
## -1027.543724 6270.848476 9644.725602 321.277376 2666.526702
## 207 208 209 210 211
## 2835.164962 5517.323395 12558.354495 -5982.064354 -11576.056444
## 212 213 214 215 216
## -5922.950704 -10832.273956 -5300.827539 1309.235921 -13232.150137
## 217 218 219 220 221
## 16189.145317 7576.395046 1279.616376 26436.820691 12224.926880
## 222 223 224 225 226
## 7014.446803 13698.916228 -4259.874368 -2070.521363 3459.377862
## 227 228 229 230 231
## 43.068403 2437.090078 8699.130728 5518.387401 -2217.309529
## 232 233 234 235 236
## -2126.234523 9138.181015 -11806.529442 -7554.990981 -8796.630476
## 237 238 239 240 241
## -10340.454379 2856.182632 1123.746938 -8528.604145 -9206.916412
## 242 243 244 245 246
## 8891.346366 -7988.641921 2276.359688 -10519.574992 -4255.997309
## 247 248 249 250 251
## 1223.912133 797.002465 -12527.845254 3450.645418 1857.340453
## 252 253 254 255 256
## 3998.126505 1909.104698 -1393.186970 10906.531759 20623.195267
## 257 258 259 260 261
## 2894.198405 -4578.069669 3815.228874 -1994.446880 3444.527819
## 262 263 264 265 266
## -5148.986636 -11177.078909 -4986.965670 -770.048866 -5436.411813
## 267 268 269 270 271
## 8539.614829 -4540.910299 3937.143276 -2370.349995 4171.238558
## 272 273 274 275 276
## 438.064021 7029.992987 -1702.487854 11738.849424 -4899.648107
## 277 278 279 280 281
## 1422.965486 -677.165016 7549.661049 -5376.666941 -3033.379523
## 282 283 284 285 286
## -11553.006662 -2928.598335 18402.643657 7481.036438 2413.301907
## 287 288 289 290 291
## -952.767802 587.893206 6081.576554 6552.155602 -19116.291399
## 292 293 294 295 296
## -11421.416334 -8367.795548 9443.199062 2820.943903 -1438.897989
## 297 298 299 300 301
## 27146.316080 9728.007588 4540.045472 9151.618574 2471.707832
## 302 303 304 305 306
## -1413.788937 7530.252472 -24674.569818 -3824.467092 -447.847886
## 307 308 309 310 311
## -7235.824283 -4213.257996 2705.373705 -9427.846801 -3434.308183
## 312 313 314 315 316
## -8380.498033 1396.530827 -3329.530560 1876.073263 -4267.108273
## 317 318 319 320 321
## 27269.219895 -1018.211930 3001.689765 10531.939760 5257.417473
## 322 323 324 325 326
## 32037.037072 4668.725686 -21375.059420 1446.601101 769.715766
## 327 328 329 330 331
## -6798.847041 -2035.527603 -33555.563116 752.179545 -2436.518053
## 332 333 334 335 336
## -220.841064 -3298.039792 3963.605499 -578.817917 -7095.958107
## 337 338 339 340 341
## -3237.293697 -2305.798869 -7791.183999 3762.586384 -1483.956906
## 342 343 344 345 346
## -1852.278715 -1108.457213 59.145194 356.848760 -1751.555220
## 347 348 349 350 351
## -9579.496830 -13313.339765 2248.131346 -4406.639297 -3735.601216
## 352 353 354 355 356
## -6053.746035 1688.743321 1302.096912 2653.128052 -3888.854677
## 357 358 359 360 361
## -633.016811 553.218095 6877.967271 104.331858 -216.044546
## 362 363 364 365 366
## 2401.767334 -2945.147777 -1061.472800 -8924.795179 -4770.354485
## 367 368 369 370 371
## -6340.264658 -5055.596933 -7344.529066 4945.170270 266.697206
## 372 373 374 375 376
## 7004.053963 -7791.754881 -2389.154415 -3510.100526 -2580.540532
## 377 378 379 380 381
## -12567.052357 1839.742389 -10717.925238 5647.730012 9247.564862
## 382 383 384 385 386
## 2983.886929 -2562.334018 1446.330388 6572.865684 11203.947328
## 387 388 389 390 391
## -6064.140792 -5599.997585 -372.246102 8347.229747 1558.068928
## 392 393 394 395 396
## 10957.643644 -10192.714747 2508.227254 435.914116 285.469089
## 397 398 399 400 401
## -929.923553 -832.745100 -14751.130152 8334.533125 -1405.597570
## 402 403 404 405 406
## -1588.259887 6774.739820 -8171.305132 -1493.485630 -2719.390427
## 407 408 409 410 411
## -5992.646661 -3003.310094 -4049.054545 -8871.473681 6053.989601
## 412 413 414 415 416
## 1521.799657 -7507.034133 -7795.844623 14146.492605 3658.493359
## 417 418 419 420 421
## 4307.246917 -8247.869355 -4917.013935 -2751.632572 2679.690947
## 422 423 424 425 426
## -14168.644795 -2883.874421 -9185.184045 2962.055324 6898.796270
## 427 428 429 430 431
## 6451.201458 -4152.070194 -4269.349859 -4854.518485 -1904.006561
## 432 433 434 435 436
## -5824.203530 -6718.497401 -6018.669826 -1445.097716 -905.942099
## 437 438 439 440 441
## -5041.832793 2526.665813 4758.645828 -5172.208105 -2257.226255
## 442 443 444 445 446
## 1478.845746 -3951.199273 2732.420532 -6703.184427 -12210.502583
## 447 448 449 450 451
## -4562.824729 9602.398367 -2132.801780 4655.472670 -5998.256671
## 452 453 454 455 456
## -1228.832890 276.229187 2910.334876 -12404.222805 3285.180319
## 457 458 459 460 461
## -6809.401391 6437.883645 2889.386052 2365.089610 -4002.642143
## 462 463 464 465 466
## 1951.489370 -161.413497 1636.809037 -687.488322 3185.518537
## 467 468 469 470 471
## -2820.378227 5637.150193 -7136.155230 -3122.917162 -2349.519809
## 472 473 474 475 476
## -4798.382630 2881.358686 7664.676507 -6187.711524 1341.948479
## 477 478 479 480 481
## -6329.066506 -2967.302792 1898.805563 -13056.144830 -9828.232231
## 482 483 484 485 486
## -1241.973194 -26.425654 -1020.656281 -1406.720906 -9653.753466
## 487 488 489 490 491
## 11057.740349 6136.875932 7287.884927 -5604.970191 5222.827508
## 492 493 494 495 496
## 9122.916519 5843.271638 -13706.473073 -10732.553058 -3559.988011
## 497 498 499 500 501
## -1212.568847 -630.448144 -7734.136683 532.105820 4199.409667
## 502 503 504 505 506
## 5395.761106 519.689359 -64.270759 -7384.967251 454.734727
## 507 508 509 510 511
## -5169.295347 1730.447832 -1411.057922 -8270.487571 -683.796746
## 512 513 514 515 516
## -2760.535061 -668.969971 1246.350823 -9593.442937 -7829.050887
## 517 518 519 520 521
## 24246.003802 9669.726161 5681.032747 -5555.895805 2606.453509
## 522 523 524 525 526
## 16818.066050 11202.873089 -24458.964760 -5251.677101 -3899.820500
## 527 528 529 530 531
## 4422.829696 -526.568919 -11270.022119 4272.798576 13768.535835
## 532 533 534 535 536
## -5179.640592 4195.654173 5359.066343 -2007.646604 -4745.412306
## 537 538 539 540 541
## -7254.477919 -2247.606180 8184.404044 -47.969022 -8315.232665
## 542 543 544 545 546
## 1679.238149 -745.489284 222.337544 -11177.183815 -11161.244998
## 547 548 549 550 551
## 1983.232439 6927.922969 -1430.827538 725.306346 -7840.046657
## 552 553 554 555 556
## 8475.161514 773.969808 -12083.292505 9072.046803 8520.807047
## 557 558 559 560 561
## -78.387728 4676.001362 -3771.578944 13929.390452 21259.546269
## 562 563 564 565 566
## -6792.573658 -9963.887000 6545.203308 -26.425861 3209.845007
## 567 568 569 570 571
## -7628.634290 -17517.446143 6535.841063 6278.857900 1720.715685
## 572 573 574 575 576
## 2912.538087 1575.256824 -2362.126468 14534.429297 -9891.768941
## 577 578 579 580 581
## -6438.250970 8548.211276 2659.354324 -6752.132108 7335.740044
## 582 583 584 585 586
## -4002.512989 -2956.689626 15536.826522 -14730.213395 8265.913359
## 587 588 589 590 591
## -126.032408 -6403.918073 -914.134761 97.528972 -10806.980296
## 592 593 594 595 596
## 1689.865951 -7261.709098 2978.618962 8759.655847 -7648.121051
## 597 598 599 600 601
## 5735.669479 2591.754917 6700.869926 -3372.886553 5984.075625
## 602 603 604 605 606
## -8487.201928 2106.235615 1112.734717 2977.771134 1324.031015
## 607 608 609 610 611
## 223.594536 -5982.056280 7931.233058 -1359.438784 -2740.252589
## 612 613 614 615 616
## -3604.746670 -8362.680022 11859.093004 4785.431184 -9483.276808
## 617 618 619 620 621
## 11499.675731 5876.173993 -5766.247165 26196.602266 -13087.730168
## 622 623 624 625 626
## -6974.054887 2998.465095 -4326.101315 -10737.546467 11195.687132
## 627 628 629 630 631
## -21782.398969 -2475.933579 8617.284496 11037.789987 -1695.565595
## 632 633 634 635 636
## 33149.541683 -6847.340900 5497.195530 5168.171270 -2508.036377
## 637 638 639 640 641
## -5562.784942 -2126.415518 -12602.508595 -2361.219002 -1996.490779
## 642 643 644 645 646
## -2624.205759 -2953.833113 1727.670878 4333.430588 16848.379405
## 647 648 649 650 651
## 18372.124358 638.104792 4552.659107 10369.132186 19881.420032
## 652 653 654 655 656
## 421.176383 -28364.217255 -1516.854615 -2452.141346 1724.152627
## 657 658 659 660 661
## -3335.763732 -10747.468215 1580.320745 4135.938894 -1111.560863
## 662 663 664 665 666
## 12932.316072 1217.161217 1671.030603 -11834.257857 1279.941047
## 667 668 669 670 671
## 1084.706108 -5270.783170 -7495.735327 2005.619124 -3781.501471
## 672 673 674 675 676
## 2613.890084 -3449.779686 -9398.617359 -8343.101336 -2997.793650
## 677 678 679 680 681
## 151.562285 2816.249759 666.361159 -3880.955662 -1856.171342
## 682 683 684 685 686
## -1366.070642 -8291.782041 4617.783626 -2294.189090 -1448.485863
## 687 688 689 690 691
## 536.194673 10795.838924 9757.056456 10504.167068 -9806.455518
## 692 693 694 695 696
## -3661.010101 -3233.049207 5788.192062 -10483.813719 -7978.368450
## 697 698 699 700 701
## -8657.881964 -6302.300300 -4757.638025 3067.697820 -4432.976636
## 702 703 704 705 706
## -1924.566941 4193.580004 31060.968992 9418.445326 23339.372076
## 707 708 709 710 711
## 1558.685085 8214.511004 22815.838360 6446.341606 -18307.255156
## 712 713 714 715 716
## 4754.361031 -5508.488753 -153.501800 430.810408 -17312.990228
## 717 718 719 720 721
## -5290.544226 3313.782485 -3038.563320 -13000.520812 4270.426019
## 722 723 724 725 726
## -5572.165213 730.780373 -3949.168876 -12459.375694 1366.305914
## 727 728 729 730 731
## -1873.192948 -9784.568952 17269.243997 1753.969497 -2746.764258
## 732 733 734 735 736
## 5693.534315 -8658.872510 -742.636391 8118.782187 -15380.462599
## 737 738 739 740 741
## -5923.911812 7399.387611 -4803.516353 145.547796 1810.560639
## 742 743 744 745 746
## -1976.367405 -5187.546556 6397.345595 -6298.558126 22681.050363
## 747 748 749 750 751
## 7785.444289 -1994.302598 -7330.738913 23383.429938 -4345.213066
## 752 753 754 755 756
## 1351.042642 -14465.551787 56082.125785 26848.344757 15011.837478
## 757 758 759 760 761
## -10728.906167 10545.100506 7252.721996 5750.250685 -46444.970833
## 762 763 764 765 766
## -16182.316797 968.502873 -2517.251810 -3456.435256 122847.067740
## 767 768 769 770 771
## 19235.195380 43643.895973 22494.396038 11984.711167 15761.366876
## 772 773 774 775 776
## 25643.862100 -98896.379032 -6774.008565 -35842.366050 1749.184006
## 777 778 779 780 781
## -1219.386914 3405.043136 -7408.882376 -1435.307088 -1935.795098
## 782 783 784 785 786
## 3434.039461 -7148.507050 -2226.339405 3930.228489 2272.988856
## 787 788 789 790 791
## -2753.047466 -4039.784976 1748.664486 2861.704029 -44.969965
## 792 793 794 795 796
## -6696.844996 -5772.038878 -1134.135643 -1250.299011 -7804.707703
## 797 798 799 800 801
## -2337.858585 -3252.265456 -2649.260282 10740.615385 2249.854675
## 802 803 804 805 806
## 7086.675343 2926.285845 -5446.549494 8193.213018 9875.875205
## 807 808 809 810 811
## -10605.810950 -7391.910818 -7495.512252 3020.030719 4205.874389
## 812 813 814 815 816
## -2260.869008 -14154.772042 -4091.788620 6279.515945 8251.718585
## 817 818 819 820 821
## -9664.312086 -7734.923547 -9316.926934 9777.777460 -1207.278173
## 822
## -4356.227526
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17246.43 20096.93 24355.87 24073.50 26429.62 23759.16 24476.31 19702.87
## 10 11 12 13 14 15 16 17
## 19439.12 16778.78 17557.15 14282.74 14333.96 14999.39 16697.94 15016.01
## 18 19 20 21 22 23 24 25
## 16051.88 15424.69 22515.38 21598.33 21077.58 22969.79 22294.99 22948.30
## 26 27 28 29 30 31 32 33
## 24796.41 18717.72 20445.88 28292.34 28350.06 28022.60 25649.24 27053.31
## 34 35 36 37 38 39 40 41
## 30900.83 31249.66 32660.48 30168.18 34142.51 37354.33 34407.01 31214.18
## 42 43 44 45 46 47 48 49
## 30060.61 20629.17 28156.65 30595.97 31686.45 38532.51 38026.51 42694.13
## 50 51 52 53 54 55 56 57
## 46935.33 39624.73 34186.86 29203.77 22340.21 28637.55 25214.20 21507.49
## 58 59 60 61 62 63 64 65
## 25920.98 27182.00 27479.36 27891.66 23757.78 40368.38 42214.48 37454.91
## 66 67 68 69 70 71 72 73
## 41666.75 46588.12 57268.71 55280.17 40506.18 38009.14 41030.56 35324.06
## 74 75 76 77 78 79 80 81
## 30771.01 21463.64 24668.84 20589.81 22678.30 17567.90 19585.67 18811.30
## 82 83 84 85 86 87 88 89
## 17837.89 15904.58 17183.83 20796.09 25218.95 26209.97 26234.96 26852.41
## 90 91 92 93 94 95 96 97
## 30982.29 29811.48 30811.99 28874.18 28073.07 28441.64 28848.69 22418.68
## 98 99 100 101 102 103 104 105
## 25437.22 18459.59 17314.39 15352.84 15652.95 16331.12 20808.96 19891.40
## 106 107 108 109 110 111 112 113
## 23406.57 23196.30 24874.36 27754.47 25249.68 21689.03 21964.77 24614.12
## 114 115 116 117 118 119 120 121
## 35491.23 33681.98 35521.59 38523.39 40481.57 38167.31 32982.32 29319.47
## 122 123 124 125 126 127 128 129
## 31404.71 29682.67 30865.59 38474.17 38116.21 37176.67 34036.56 35819.82
## 130 131 132 133 134 135 136 137
## 41223.96 40663.61 31855.12 33121.38 36314.83 32721.32 31106.73 30190.67
## 138 139 140 141 142 143 144 145
## 26743.71 28159.78 27929.14 25612.76 27639.53 26262.69 19857.42 22891.45
## 146 147 148 149 150 151 152 153
## 20715.63 23696.18 24236.80 25829.03 26011.32 27667.39 28969.64 32005.29
## 154 155 156 157 158 159 160 161
## 27470.24 26732.34 24284.77 30185.67 41648.22 39976.52 37338.42 42364.49
## 162 163 164 165 166 167 168 169
## 43774.69 47194.54 42655.23 37957.00 43388.50 59709.49 61924.17 60336.43
## 170 171 172 173 174 175 176 177
## 57158.47 55555.87 58261.56 57284.53 49567.76 52394.52 56141.46 56177.69
## 178 179 180 181 182 183 184 185
## 63314.74 53806.09 50553.29 41351.36 32880.95 36393.26 46508.37 45963.76
## 186 187 188 189 190 191 192 193
## 51923.37 57674.97 68470.38 73759.32 67446.75 67640.40 74718.86 70390.32
## 194 195 196 197 198 199 200 201
## 65907.99 55139.44 49162.71 50588.64 46178.28 38338.07 44770.21 42995.56
## 202 203 204 205 206 207 208 209
## 42633.12 43112.01 49913.85 58813.29 58442.47 60169.26 61826.96 65622.50
## 210 211 212 213 214 215 216 217
## 75099.92 67173.63 55349.09 49951.70 40937.68 37891.91 41009.15 31017.85
## 218 219 220 221 222 223 224 225
## 48010.89 55340.10 56243.04 79034.64 86538.27 88543.80 96143.87 87084.38
## 226 227 228 229 230 231 232 233
## 81075.91 80657.36 77303.48 76464.01 81206.47 82572.31 77001.38 72208.82
## 234 235 236 237 238 239 240 241
## 77868.96 64501.42 56528.77 48470.17 40072.10 44268.82 46424.03 39867.20
## 242 243 244 245 246 247 248 249
## 33539.51 43833.78 38074.07 42014.29 34269.28 32973.66 36633.14 39460.27
## 250 251 252 253 254 255 256 257
## 30279.21 36224.09 40029.87 45230.61 47952.04 47444.04 57756.80 75274.09
## 258 259 260 261 262 263 264 265
## 75088.93 68391.91 69875.45 66091.90 67539.70 61290.22 50552.54 46575.33
## 266 267 268 269 270 271 272 273
## 46784.98 42887.24 51701.48 47970.29 52121.78 50236.19 54308.22 54604.58
## 274 275 276 277 278 279 280 281
## 60628.92 58260.44 67944.51 61862.32 62072.59 60419.77 66169.24 59892.52
## 282 283 284 285 286 287 288 289
## 56452.44 45992.74 44387.64 61639.68 67176.13 67586.05 65000.68 64086.99
## 290 291 292 293 294 295 296 297
## 68092.56 72007.29 52981.99 43072.65 37076.80 47410.06 50655.61 49768.54
## 298 299 300 301 302 303 304 305
## 73992.71 79944.95 80613.38 85231.15 83427.65 78452.18 81923.00 56792.90
## 306 307 308 309 310 311 312 313
## 53049.71 52729.11 46512.12 43718.34 47325.85 39869.45 38590.07 33145.33
## 314 315 316 317 318 319 320 321
## 36934.24 36114.64 39950.54 37932.64 63748.78 61587.45 63212.92 71220.30
## 322 323 324 325 326 327 328 329
## 73610.39 99121.56 97497.35 73299.54 72096.00 70451.42 62393.81 59512.71
## 330 331 332 333 334 335 336 337
## 29426.25 33118.09 33558.13 35880.75 35220.82 40994.53 42071.39 37313.44
## 338 339 340 341 342 343 344 345
## 36526.94 36653.76 31967.27 37973.24 38637.42 38896.17 39773.00 41561.01
## 346 347 348 349 350 351 352 353
## 43385.13 43136.50 36072.91 26629.73 31980.64 30840.32 30429.89 28043.54
## 354 355 356 357 358 359 360 361
## 32727.90 36486.59 40955.43 39142.30 40404.07 42545.03 49948.95 50500.19
## 362 363 364 365 366 367 368 369
## 50702.09 53168.15 50648.62 50092.51 42729.07 39922.55 36095.03 33871.10
## 370 371 372 373 374 375 376 377
## 29924.26 37220.73 39510.37 47405.18 41369.73 40816.24 39351.83 38884.05
## 378 379 380 381 382 383 384 385
## 29740.97 34344.50 27387.98 35617.01 45962.26 49531.91 47803.24 49797.28
## 386 387 388 389 390 391 392 393
## 56024.77 65521.43 58724.71 53186.39 52914.77 60303.07 60827.07 69506.00
## 394 395 396 397 398 399 400 401
## 58598.77 60167.51 59727.10 59210.35 57695.46 56455.56 43198.47 51794.31
## 402 403 404 405 406 407 408 409
## 50793.55 49758.55 56167.45 48701.06 48011.39 46336.08 42008.17 40837.48
## 410 411 412 413 414 415 416 417
## 38899.05 32986.15 40868.34 43798.18 38464.13 33546.51 48435.94 52285.32
## 418 419 420 421 422 423 424 425
## 56219.30 48679.44 44998.35 43672.74 47263.50 35668.73 35397.61 29649.52
## 426 427 428 429 430 431 432 433
## 35246.06 43583.66 50484.07 47245.64 44310.80 41232.29 41120.35 37593.93
## 434 435 436 437 438 439 440 441
## 33727.67 30958.38 32536.37 34387.98 32390.19 37262.21 43475.21 40223.65
## 442 443 444 445 446 447 448 449
## 39929.30 42939.34 40822.87 44817.18 40058.36 31079.82 29915.89 41286.52
## 450 451 452 453 454 455 456 457
## 40967.67 46625.69 42256.55 42606.63 44229.09 47951.79 37813.82 42668.97
## 458 459 460 461 462 463 464 465
## 38086.69 45664.90 49189.20 51812.93 48538.51 50882.13 51083.91 52833.06
## 466 467 468 469 470 471 472 473
## 52330.05 55277.38 52602.42 57659.73 50911.49 48519.52 47103.95 43724.21
## 474 475 476 477 478 479 480 481
## 47484.89 54957.28 49377.48 51082.78 45865.30 44242.34 47078.72 36480.09
## 482 483 484 485 486 487 488 489
## 30033.83 31905.43 34605.37 36097.15 37064.18 30697.26 43242.70 49910.97
## 490 491 492 493 494 495 496 497
## 56749.54 51454.60 56293.51 63936.44 67752.47 53992.12 44558.56 42581.14
## 498 499 500 501 502 503 504 505
## 42904.73 43696.85 38176.89 40578.73 45886.67 51575.17 52285.70 52396.40
## 506 507 508 509 510 511 512 513
## 46090.69 47432.30 43686.98 46445.77 46111.06 39819.23 40951.68 40125.83
## 514 515 516 517 518 519 520 521
## 41232.79 43876.01 36707.48 31981.14 55899.70 64070.25 67727.61 61098.69
## 522 523 524 525 526 527 528 529
## 62439.79 76041.84 83026.96 57946.96 52810.82 49501.17 53885.43 53391.16
## 530 531 532 533 534 535 536 537
## 43562.92 48560.75 61236.50 55750.77 59152.51 63145.08 60194.13 55218.91
## 538 539 540 541 542 543 544 545
## 48673.32 47327.60 55274.25 55024.38 47575.48 49801.77 49628.23 50322.90
## 546 547 548 549 550 551 552 553
## 40960.67 32786.62 37133.65 45259.97 45056.69 46764.62 40767.27 49791.03
## 554 555 556 557 558 559 560 561
## 50947.72 40714.67 50267.05 58139.24 57503.43 61105.44 56867.61 68642.17
## 562 563 564 565 566 567 568 569
## 85350.72 75429.89 63979.80 68404.28 66526.44 67714.49 59274.45 43244.44
## 570 571 572 573 574 575 576 577
## 50261.43 56173.57 57357.75 59435.74 60083.56 57206.57 69467.77 58828.54
## 578 579 580 581 582 583 584 585
## 52544.07 60154.65 61660.42 54746.26 61020.23 56591.12 53632.17 67218.36
## 586 587 588 589 590 591 592 593
## 52629.66 59982.60 59073.92 52788.71 52093.04 52369.41 43074.28 45874.42
## 594 595 596 597 598 599 600 601
## 40494.52 44745.34 53518.98 46842.33 52708.25 55088.84 60764.60 56918.21
## 602 603 604 605 606 607 608 609
## 61737.63 53296.34 55178.55 55955.80 58266.68 58841.41 58381.63 52552.20
## 610 611 612 613 614 615 616 617
## 59622.15 57679.97 54773.75 51475.97 44430.62 55954.43 59846.42 50771.18
## 618 619 620 621 622 623 624 625
## 61185.40 65375.25 58857.40 81111.02 66216.34 58536.68 60541.96 55889.83
## 626 627 628 629 630 631 632 633
## 46213.88 56933.83 37467.36 37327.43 46906.92 57401.85 55444.17 84206.77
## 634 635 636 637 638 639 640 641
## 74381.52 76584.83 78224.04 72944.21 65654.99 62285.37 50176.22 48542.63
## 642 643 644 645 646 647 648 649
## 47432.92 45913.40 44296.19 46976.14 51598.91 66587.16 81028.18 78148.20
## 650 651 652 653 654 655 656 657
## 79053.01 84931.29 98391.54 93144.07 63379.71 60828.57 57779.42 58765.19
## 658 659 660 661 662 663 664 665
## 55202.04 45603.68 47990.78 52313.56 51504.83 63079.98 62957.54 63247.40
## 666 667 668 669 670 671 672 673
## 51689.49 53050.58 54070.21 49403.59 43376.38 46414.79 44010.82 47501.64
## 674 675 676 677 678 679 680 681
## 45251.47 38080.82 32732.65 32730.15 35482.32 40219.78 42482.81 40485.03
## 682 683 684 685 686 687 688 689
## 40508.64 40957.92 35293.79 41630.47 41127.34 41426.95 43424.73 54144.80
## 690 691 692 693 694 695 696 697
## 62611.83 70670.31 59954.87 55958.05 52836.81 57996.81 48278.51 41970.31
## 698 699 700 701 702 703 704 705
## 35859.01 32574.35 31052.59 36565.55 34827.14 35500.56 41440.32 70132.70
## 706 707 708 709 710 711 712 713
## 76298.34 93865.60 90180.63 92778.88 107821.23 106660.54 83996.50 84344.20
## 714 715 716 717 718 719 720 721
## 75672.64 72772.05 70746.28 53456.26 48849.36 52345.42 49847.38 38950.15
## 722 723 724 725 726 727 728 729
## 44524.45 40791.51 43039.17 40911.95 31608.69 35563.91 36189.85 29818.18
## 730 731 732 733 734 735 736 737
## 47906.32 50156.48 48188.18 53848.44 46246.49 46521.36 54511.75 40948.05
## 738 739 740 741 742 743 744 745
## 37356.04 45866.80 42637.74 44142.01 46913.80 46025.98 42441.08 49437.70
## 746 747 748 749 750 751 752 753
## 44453.24 65438.84 70765.02 66870.02 58796.43 78597.36 71663.96 70581.98
## 754 755 756 757 758 759 760 761
## 55802.87 104576.80 121666.16 126260.19 107765.76 110196.71 109443.32 107470.40
## 762 763 764 765 766 767 768 769
## 60096.17 45130.78 47042.11 45665.15 43639.50 152330.09 156771.82 182003.75
## 770 771 772 773 774 775 776 777
## 185574.15 179505.20 177500.42 184390.09 81495.58 72074.51 38412.53 41849.24
## 778 779 780 781 782 783 784 785
## 42258.67 46661.17 41053.88 41374.22 41216.67 45775.22 40506.77 40203.91
## 786 787 788 789 790 791 792 793
## 45323.44 48351.48 46604.07 43950.48 46692.15 50063.40 50469.70 45007.47
## 794 795 796 797 798 799 800 801
## 41039.14 41624.73 42035.28 36662.00 36743.84 36015.69 35906.24 47521.00
## 802 803 804 805 806 807 808 809
## 50253.18 56872.86 59023.69 53582.07 60751.98 68494.24 57352.63 50419.23
## 810 811 812 813 814 815 816 817
## 44264.83 48078.98 52451.87 50620.63 38616.93 36919.63 44505.71 52865.17
## 818 819 820 821 822
## 44507.21 38884.93 32584.22 43773.56 43952.23
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8108
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.93739 0.7672301 3.877592
## t2* 2650.70239 163.1038484 871.833489
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.074012 4.893292 13.16196
## 2 lag_depvar 1613.499244 2698.881342 4430.49604Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Mar 10 00:45:47 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Mar 10 00:45:56 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Mar 10 00:46:05 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Mar 10 00:46:15 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Mar 10 00:46:24 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Mar 10 00:46:33 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Mar 10 00:46:43 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Mar 10 00:46:52 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Mar 10 00:47:01 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Mar 10 00:47:11 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 0.0000 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 258.8850 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 59.9440 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 0.0000 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 0.0000 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 36.9995 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 10.9950 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.0000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 366.8235 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2624, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2624 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.2## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.2##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-03-09 00:04:58 sería de: 26.921 pesos// Percentil 95% más alto proyectado: 35.097,94
Según TimeGPT: La proyección de la UF a 298 días más 2026-01-01 sería de: 39.556,3 pesos// Percentil 80% más alto proyectado: 39.946,39 pesos// Percentil 95% más alto proyectado: 41.030,09
Según prophet: La proyección de la UF a 298 días más 2026-01-01 sería de: 38.446 pesos// Percentil 95% más alto proyectado: 41.042
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26537.82 | 26331.66 |
| Lo.80 | 26669.99 | 26493.43 |
| Point.Forecast | 26921.46 | 26799.01 |
| Hi.80 | 31593.20 | 32074.20 |
| Hi.95 | 34386.03 | 34866.72 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,1,1)
##
## Coefficients:
## ar1 ma1
## 0.4188 -0.9485
## s.e. 0.1294 0.0592
##
## sigma^2 = 37557: log likelihood = -481.1
## AIC=968.2 AICc=968.55 BIC=975.03paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.3717 422.8030 19.5217
## s.e. 0.0968 263.1033 8.1517
##
## sigma^2 = 34940: log likelihood = -483.97
## AIC=975.93 AICc=976.52 BIC=985.09forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 703.6388 | 643.7799 | 629.0613 |
| Lo.80 | 838.9262 | 795.5792 | 723.3464 |
| Point.Forecast | 1094.4902 | 1083.7927 | 948.1711 |
| Hi.80 | 1350.0541 | 1375.4911 | 1241.8993 |
| Hi.95 | 1485.3415 | 1529.9070 | 1432.1579 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.5 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.12
## [10] tidytext_0.4.2 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [16] xts_0.14.1 forecast_8.23.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.8.17 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.1 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.8.6 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-13 stringr_1.5.1
## [43] stringi_1.8.4 DataExplorer_0.8.3 data.table_1.16.4
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.0
## [4] httr2_1.1.0 lifecycle_1.0.4 StanHeaders_2.32.10
## [7] doParallel_1.0.17 globals_0.16.3 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.0.2 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.9 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] doRNG_1.8.6.1 askpass_1.2.1 pkgbuild_1.4.6
## [22] DBI_1.2.3 abind_1.4-8 quadprog_1.5-8
## [25] nnet_7.3-19 rappdirs_0.3.3 sandwich_3.1-1
## [28] inline_0.3.21 tokenizers_0.3.0 listenv_0.9.1
## [31] anytime_0.3.11 performance_0.13.0 spatial_7.3-17
## [34] parallelly_1.42.0 codetools_0.2-20 xml2_1.3.6
## [37] tidyselect_1.2.1 ggeffects_2.2.0 farver_2.1.2
## [40] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [43] stats4_4.4.0 jsonlite_1.8.9 ellipsis_0.3.2
## [46] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.1
## [49] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [52] xfun_0.50 TTR_0.24.4 ggfortify_0.4.17
## [55] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [58] fastmap_1.2.0 boot_1.3-30 openssl_2.3.2
## [61] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [64] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [67] networkD3_0.4 gtools_3.9.5 generics_0.1.3
## [70] htmlwidgets_1.6.4 ggstats_0.8.0 pkgconfig_2.0.3
## [73] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [76] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [79] carData_3.0-5 tseries_0.10-58 snakecase_0.11.1
## [82] knitr_1.49 rstudioapi_0.17.1 tzdb_0.4.0
## [85] uuid_1.2-1 nlme_3.1-164 curl_6.2.0
## [88] cachem_1.1.0 sjlabelled_1.2.0 KernSmooth_2.23-22
## [91] parallel_4.4.0 fBasics_4041.97 pillar_1.10.1
## [94] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [97] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [100] evaluate_1.0.3 mvtnorm_1.3-3 cli_3.6.4
## [103] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [106] future.apply_1.11.3 labeling_0.4.3 sjmisc_2.8.10
## [109] rstan_2.32.6 QuickJSR_1.5.1 viridisLite_0.4.2
## [112] assertthat_0.2.1 munsell_0.5.1 lazyeval_0.2.2
## [115] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [118] bit64_4.6.0-1 future_1.34.0 nixtlar_0.6.2
## [121] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [124] bslib_0.9.0 quantmod_0.4.26 bit_4.5.0.1#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))